Real-time motion analytics during brain MRI improve data quality and reduce costs.

Area of Science:

• Neuroimaging
• Medical Physics
• Biomedical Engineering

Background:

• Head motion is a significant confounder in structural and functional brain MRI studies.
• Excluding motion-corrupted data frames can lead to substantial data loss, especially in pediatric cohorts.
• Current methods like collecting buffer data are costly and do not guarantee sufficient high-quality data.

Purpose of the Study:

• To develop and evaluate an easy-to-use software suite for real-time head motion monitoring during MRI scans.
• To enable the collection of sufficient high-quality MRI data while minimizing scan time and costs.
• To provide scanner operators with real-time motion analytics for adaptive data acquisition.

Main Methods:

• Development of the Framewise Integrated Real-time MRI Monitoring (FIRMM) software.
• Implementation of real-time head motion analytics for scanner operators.
• Utilizing FIRMM to determine optimal scan duration based on motion criteria for individual participants.

Main Results:

• FIRMM software provides real-time head motion analytics.
• The software allows for adaptive scanning until desired low-movement data is acquired.
• Utilizing FIRMM reduced total brain MRI scan times and associated costs by 50% or more.

Conclusions:

• Real-time motion monitoring with FIRMM is an effective strategy to improve MRI data quality.
• FIRMM facilitates efficient data acquisition, reducing scan time and costs in clinical and research settings.
• This approach offers a practical solution to mitigate motion artifacts in brain MRI, particularly for challenging populations.